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Combining Immunomics and Genomics for the Analysis of the Microenvironment of Colorectal Cancer Liver Metastases

Kosaloglu, Zeynep

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Abstract

Cancer immunotherapies have recently shown outstanding clinical results in a number of patients across various tumor types. However, currently only a fraction of patients responds to immunotherapy, and it is a major concern to understand the underlying mechanisms. The composition of the tumor microenvironment has been shown to have an important impact on tumor growth and progression, as well as on response to therapy. It has been reported that the type and density of tumor-infiltrating immune cells are highly predictive for disease outcome in various cancers. These studies have also suggested that a high density of tumor-infiltrating lymphocytes is strongly correlated with mutational load. One hypothesis in this context is that somatic mutations found in cancer cells may give rise to novel epitopes, so-called neoepitopes, which attract and keep lymphocytes at the tumor site. Neoepitopes have also been suggested to be crucial for the outcome of immune checkpoint therapies, as it was reported that cancers with a high mutational load respond best to checkpoint therapy. An explanation for this is that mutations give rise to neoepitopes that can be targeted by specific T cells following their release from inhibitory signals.

It has now become evident that effective immunotherapies have to be tailored to the specific immune setting of each tumor. The complex interplay between the tumor and the immune system has to be systematically analyzed for characterizing patients and identifying therapies they will most likely benefit from. This highly personalized approach requires the integrated analysis of numerous tumor and host factors. Accordingly, the main aim of this PhD project was the establishment of an integrated analysis pipeline to obtain detailed data about tumor-host interactions, including analysis of the mutational and neoepitope load, the type and densities of tumor-infiltrating immune cells, the expression of immunological markers, and the expression of specific cytokines. This analysis pipeline combines available genomic and immunomic resources and adds further depth into the analysis by additional computational pipelines. The already well established sequencing and somatic mutation detection pipelines that have been developed in the DKFZ bioinformatics departments (Prof. Roland Eils and Prof. Benedikt Brors) were integrated with the cytokine profiling and histological analysis workflows in Professor Jäger's group (NCT, Medical Oncology). Additional computational pipelines for HLA genotyping from sequencing data, as well as for epitope predictions for HLA class I and class II were implemented and included. Taken together these pipelines provide a broad picture of tumor-host interactions. The established analysis pipeline allows the rapid and systematic analysis of large patient cohorts.

Professor Jäger's group has been collecting colorectal cancer (CRC) liver metastases and systematically characterizing their immune cell infiltration and cytokine profiles, as well as the correlation to clinical outcome. In these studies it was shown that in general, there are at least two patient groups for each CRC stage: patients with high infiltrate density and patients with low infiltrate density, with the latter having a much worse prognosis. A patient cohort including 10 patients with high densities of infiltrating lymphocytes (TIL-high) and 10 patients with low densities (TIL-low) was assembled and provided for analysis in this PhD project. The described integrated analysis pipeline was developed using this patient cohort.

The established analysis pipeline was then used to systematically investigate TIL-high versus TIL-low CRC metastases in order to assess the correlation of mutational and neoepitope load to lymphocyte infiltration and whether additional factors distinguishing the two groups can be discovered. The results show that the mutational and neoepitope load is not significantly different between patients with high and patients with low lymphocyte density in the analyzed patient cohort. Although a trend can be observed in a way that the TIL-high group seems to be enriched for mutations and neoepitopes, no statistical significance was detectable. Instead, the cytokine expression profiles are clearly distinct between the two subgroups: CXCL12, CXCL9, CCL7, CCL27, IL-17, IL-13, IL-7, IL-4, IFNg, GM-CSF, HGF, and TRAIL are significantly overexpressed in the TIL-high group. Interestingly both, pro-tumorigenic as well as anti-tumorigenic factors are overexpressed in the TIL-high group. Histological analysis additionally revealed that the TIL-high samples are enriched for macrophages. Furthermore, PD-L1, the ligand for the inhibitory immune checkpoint protein PD-1, is overexpressed in the majority of TIL-high samples when compared to the TIL-low samples. These results indicate that the immune contexture at the metastatic lesion seems to be a stronger factor for lymphocyte infiltration than the mutational and neoepitope landscape.

The established integrated analysis pipeline has already been applied in the clinic to conduct case studies with several patients being treated at the NCT. Patients with refractory and rare cancers were extensively analyzed for their genomic and immunomic features, which enabled the exploration of additional immunotherapeutic strategies. In doing so, a working logistics for the clinical setting was established, and the results provided insights into the feasibility of the approach. Based on these findings, clinical studies with neoepitope-based vaccines are currently under development in Professor Jäger's group, and the predictive impact of the newly established integrated analysis pipeline will be evaluated in prospective clinical trials.

Item Type: Dissertation
Supervisor: Jäger, Prof. Dr. Dirk
Date of thesis defense: 1 June 2016
Date Deposited: 17 Jun 2016 07:58
Date: 2016
Faculties / Institutes: The Faculty of Bio Sciences > Dean's Office of the Faculty of Bio Sciences
Subjects: 000 Generalities, Science
004 Data processing Computer science
570 Life sciences
610 Medical sciences Medicine
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